Project Members

Leonardo Lancia

Statistical modeling of trajectories and shapes applied to speech

Speech unfolds in time and there are many reasons to move away from a static characterization of speech patterns. This is a technical issue with deep theoretical implications, because static characterizations are not informative enough if we want to study speech in a dynamical framework. To model speeech data as multivariate trajectories, we followed Morriss and Carroll (2006) and used functional mixed models based on isomorphic transformations of the oberved data. Curves or images were represented by configurations of independent coefficients. These coefficients were the dependent variables in multivariate Bayesian mixed models with fixed and random factors.